Big Picture/Overview

Where does model evaluation and selection come up during an introductory machine learning course?

  1. Early on, maybe first day — How do we know if a model is any good?

  2. During model building/training — How to pick the “best” model?

  3. Discussion of final model — How will the final model preform?

Basic structure

Who is working on it, where is data coming from, where it will be located

Examples

How do we know if a model is any good?